CN112701724B - Fan control system - Google Patents

Fan control system Download PDF

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Publication number
CN112701724B
CN112701724B CN202011529947.3A CN202011529947A CN112701724B CN 112701724 B CN112701724 B CN 112701724B CN 202011529947 A CN202011529947 A CN 202011529947A CN 112701724 B CN112701724 B CN 112701724B
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China
Prior art keywords
power
real
time
power grid
grid
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CN112701724A (en
Inventor
韦永清
冯胜东
梅乐
孙佳林
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Shanghai Electric Wind Power Group Co Ltd
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Shanghai Electric Wind Power Group Co Ltd
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Priority to CN202011529947.3A priority Critical patent/CN112701724B/en
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/022—Adjusting aerodynamic properties of the blades
    • F03D7/0224—Adjusting blade pitch
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/028—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power
    • F03D7/0284—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power in relation to the state of the electric grid
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/044—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with PID control
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/046—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P9/00—Arrangements for controlling electric generators for the purpose of obtaining a desired output
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P9/00—Arrangements for controlling electric generators for the purpose of obtaining a desired output
    • H02P9/04—Control effected upon non-electric prime mover and dependent upon electric output value of the generator
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2101/00—Special adaptation of control arrangements for generators
    • H02P2101/15—Special adaptation of control arrangements for generators for wind-driven turbines
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Chemical & Material Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Fuzzy Systems (AREA)
  • Fluid Mechanics (AREA)
  • Control Of Eletrric Generators (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention discloses a fan control system, which comprises a control device, wherein the control device comprises a central processing unit and a neural network processor; the central processing unit executes a real-time task and is used for collecting and processing real-time data of the fan equipment and sending the processed real-time data to the neural network processor; the neural network processor executes a non-real-time task and is used for analyzing the historical data to generate auxiliary data and sending the auxiliary data to the central processing unit; the central processing unit is used for generating target data according to the auxiliary data and the real-time data and applying the target data to the fan equipment. In the invention, the defects of mutually independent functions and difficult data integration of each subsystem can be overcome through the interaction process of the fan equipment and the fan control system, and the original hardware architecture is changed by only writing in the original control device, so that the new functions are simply added.

Description

Fan control system
Technical Field
The invention relates to the field of industrial control, in particular to a fan control system.
Background
At present, in the field of control of fans, a plurality of sets of hardware systems are usually arranged in a fan system, and each new function is added in the fan system, so that a new data acquisition system, a controller and other hardware systems are required to be added on the original hardware system, each system is required to be communicated with a fan main control system through a conventional communication protocol, the burden of the fan main control system is increased, the functions of the systems are relatively independent, and later data integration is difficult and the newly added functions are complex.
Disclosure of Invention
The invention aims to overcome the defects that in the prior art, each subsystem in a fan control system is relatively independent, the newly added function difficulty is high, and the data integration of each subsystem is difficult, and provides a fan control system which uses the same hardware platform, has simple data integration and small newly added function difficulty.
The invention solves the technical problems by the following technical scheme:
The embodiment provides a fan control system, which comprises a control device, wherein the control device comprises a central processing unit and a neural network processor;
The central processing unit executes a real-time task and is used for collecting and processing real-time data of the fan equipment, and the central processing unit is also used for sending the real-time data processed at the current moment to the neural network processor;
The neural network processor performs a non-real-time task for analyzing historical data to generate auxiliary data and transmitting the auxiliary data to the central processor, wherein the historical data is the real-time data from the central processor after a plurality of processes received at a plurality of moments before the current moment;
The central processing unit is also used for generating target data according to the auxiliary data and the real-time data and acting the target data on the fan equipment to control the fan equipment to operate.
Preferably, the fan control system further comprises a cloud platform, and the neural network processor is further used for sending the analyzed data to the cloud platform;
The cloud platform is used for carrying out auxiliary operation on the analyzed data to generate auxiliary data, and is also used for sending the auxiliary data to the neural network processor.
Preferably, the fan device comprises a generator and a converter, wherein the generator is connected with the converter, and the converter is connected with a power grid; the target data comprises PWM (pulse width modulation) power control signals for controlling real-time power of the grid.
Preferably, the central processing unit is used for collecting a first real-time output current of the generator, a second real-time output current of the converter and real-time power of the power grid;
The neural network processor is used for calculating the reference power of the power grid according to the historical power of the power grid;
the central processing unit is used for acquiring the PWM power control signal according to the first real-time output current, the second real-time output current and the difference between the reference power of the power grid and the real-time power of the power grid.
Preferably, the current transformer comprises a machine side current transformer and a net side current transformer, the machine side current transformer is connected with the net side current transformer, the generator is connected with the machine side current transformer, and the net side current transformer is connected with the power grid;
The second real-time output current is the output current of the grid-side converter;
the central processing unit is also used for collecting the real-time output voltage of the machine side converter;
The neural network processor is further used for calculating a reference output voltage of the machine side converter according to the historical output voltage of the machine side converter;
The central processing unit is used for acquiring the PWM power control signal according to the first real-time output current, the second real-time output current, the difference between the reference power of the power grid and the real-time power of the power grid, and the difference between the reference output voltage of the power grid and the real-time output voltage.
Preferably, the reference power of the power grid comprises active reference power and reactive reference power, and the real-time power of the power grid comprises active real-time power and reactive real-time power;
The neural network processor is used for calculating the reference active power of the power grid according to the historical active power of the power grid, and calculating the reference reactive power of the power grid according to the historical reactive power of the power grid;
The central processing unit is used for acquiring the PWM power control signal according to the first real-time output current, the second real-time output current, the difference between the active reference power of the power grid and the active real-time power of the power grid, the difference between the reactive reference power of the power grid and the reactive real-time power of the power grid, and the difference between the reference output voltage and the real-time output voltage.
Preferably, the fan apparatus comprises a blade and the target data comprises a pitch angle of the blade.
Preferably, the central processing unit is used for collecting real-time power of the power grid and real-time rotation speed of the blades;
The neural network processor is used for calculating the reference power of the power grid according to the historical power of the power grid and calculating the reference rotating speed of the fan equipment according to the historical rotating speed of the fan equipment;
The central processing unit is also used for acquiring the pitch angle of the blade according to the difference between the reference rotating speed of the fan equipment and the real-time rotating speed of the fan equipment and the difference between the reference power of the power grid and the real-time power of the power grid.
Preferably, the reference power of the power grid comprises active reference power and reactive reference power, and the real-time power of the power grid comprises active real-time power and reactive real-time power;
The neural network processor is used for calculating the reference active power of the power grid according to the historical active power of the power grid, and calculating the reference reactive power of the power grid according to the historical reactive power of the power grid;
The central processing unit is further used for obtaining the pitch angle of the blade according to the difference between the reference rotating speed of the fan equipment and the real-time rotating speed of the fan equipment, the difference between the reference active power of the power grid and the real-time active power of the power grid and the difference between the reference reactive power of the power grid and the real-time reactive power of the power grid.
Preferably, the central processor and the neural network processor communicate through a bottom bus.
The invention has the positive progress effects that: in the invention, a unified control device for hardware control is arranged, a central processing unit for executing real-time tasks and a neural network processor for executing non-real-time tasks are specifically arranged in the control device, the central processing unit can acquire and process real-time data of fan equipment, the neural network processor can further analyze the processed data to generate auxiliary data and transmit the auxiliary data back to the central processing unit, the central processing unit can utilize the auxiliary data and the real-time data to generate target data to control the fan equipment to run so as to supply power to a power grid.
Drawings
Fig. 1 is a schematic block diagram of a fan control system according to embodiment 1 of the present invention.
Fig. 2 is a schematic block diagram of a fan control system according to embodiment 2 of the present invention.
Fig. 3 is a schematic block diagram of a fan apparatus according to embodiment 3 of the present invention.
Fig. 4 is a schematic diagram of a control principle of the fan control system in embodiment 4 of the present invention in a specific scenario.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention.
Example 1
The present embodiment provides a fan control system, which includes a control device 20 as shown in fig. 1, where the control device 20 includes a central processor 201 and a neural network processor 202.
The central processing unit 201 is mainly used for executing a real-time task, wherein the real-time task comprises collecting and processing real-time data of the fan device 10, and the real-time task further comprises sending the real-time data processed at the current moment to the neural network processor 202, and the fan device 10 is mainly used for supplying power to a power grid.
Specifically, the central processing unit 201 may sense the real-time state of the fan apparatus 10, collect sensed data in real time, perform real-time data management to control the fan apparatus 10 in real time, perform real-time fault processing and alarm on the fan apparatus 10, and so on.
The neural network processor 202 accepts fan data from the central processor 201, can analyze historical data based on digital twinning to generate auxiliary data, and sends the auxiliary data to the central processor 201, wherein the historical data is a plurality of processed real-time data from the central processor 201 received at a plurality of times before the current time.
In particular, the neural network processor 202 may perform data analysis applications, artificial intelligence algorithm applications, digital twinning applications, multivariate data management, remote data management, etc. on a large amount of data collected prior to the current time of the central processor 201 to generate the assistance data.
The cpu 201 and the neural network processor 202 may communicate in real time, and in this embodiment, the communication is preferably performed by means of a bottom bus, so as to enhance the security and stability of the communication between the cpu 201 and the neural network processor 202. The data collected by the central processor 201 may be transmitted to the neural network processor 202, and the application analysis result performed by the neural network processor 202, that is, the auxiliary data, may be safely and reliably transmitted to the central processor 201.
The central processor 201 is further configured to generate target data based on the auxiliary data generated by the neural network processor 202 and the real-time data from the fan apparatus 10, and apply the target data to the fan apparatus 10 to control the operation of the fan apparatus 10.
In this embodiment, a unified control device 20 for hardware control is provided, and a central processing unit 201 for real-time tasks and a neural network processor 202 for executing non-real-time tasks are specifically provided in the control device 20, so that the central processing unit 201 can collect and process real-time data of the fan device 10, the neural network processor 202 can further analyze the processed data to generate auxiliary data, and transmit the auxiliary data back to the central processing unit 201, and the central processing unit 201 can utilize the auxiliary data and the real-time data to generate target data to control the fan device 10 to operate so as to supply power to the power grid. In this embodiment, the interaction process of the central processor 201 and the neural network processor 202 can overcome the defects of the prior art that the functions of the subsystems are mutually independent and the data integration is difficult, and in this embodiment, when a new function needs to be added, no additional hardware device is needed, only the writing of the new function needs to be performed in the original control device 20, in this embodiment, the hardware architecture of the original fan control system is changed, so that the addition of the new function in the fan control system does not need to be performed, and the addition of the new function becomes simple.
Example 2
The embodiment provides a fan control system, the embodiment is based on embodiment 1, as shown in fig. 2, the fan control system in the embodiment further includes a cloud platform 30, the neural network processor 202 is further configured to send the analyzed data to the cloud platform 30, the neural network processor 202 is seamlessly connected with the cloud platform 30, the cloud platform 30 is configured to perform an auxiliary operation on the analyzed data to generate auxiliary data, specifically, the main function of the cloud platform 30 includes APP (application program) design and deployment, the cloud platform 30 has a massive resource, and can perform complex operation, generate auxiliary data according to an operation result, and transmit the auxiliary data to the neural network processor 202 through secure communication.
In this embodiment, the cloud platform 30 connected to the neural network processor 202 may perform a series of complex operations, and may perform data interaction with other systems and networks, so that on one hand, the operation efficiency of the local fan control system is improved, and on the other hand, the functionality of the overall fan control system is also improved.
In this embodiment, the auxiliary data generated by the cloud end and the auxiliary data generated by the neural network processor 202 are superimposed for being transmitted back to the central processor 201 to generate the target data.
Example 3
The present embodiment provides a fan control system, which is based on embodiment 1 or embodiment 2, and in this embodiment, a fan device 10 includes a generator and a converter, the generator is connected with the converter, and the converter is connected with a power grid.
Fig. 3 shows a schematic diagram of a fan assembly 10 in one embodiment, comprising a blade, a drive train, a generator, a machine side converter, a grid side converter, a filter and a transformer, wherein the blade rotates under the action of wind force, thereby transferring wind energy to the blade, and the blade outputs electrical energy to a power grid after passing through the drive train, the generator, the machine side converter, the grid side converter, the filter and the transformer.
In this embodiment, the target data includes a PWM power control signal, where the PWM power control signal is used to control the real-time power of the power grid, and the central processor 201 may specifically include a generator control module PID, which is used to control the real-time power of the power grid through the PWM power control signal.
The generator control module PID is configured to collect a first real-time output current of the generator, a real-time output voltage of the machine side converter, a second real-time output current of the grid side converter, and a real-time power of the power grid, where the real-time power of the power grid may include an active real-time power and a reactive real-time power.
The neural network processor 202 is configured to calculate the reference power of the power grid according to the historical power of the power grid, for example, the average value of the power grid during the normal operation of the history may be calculated as the reference power, the reference power may be calculated by other existing algorithms, the reference output voltage of the side converter may be calculated according to the historical output voltage of the side converter, and the reference power of the power grid may be calculated according to the historical power of the power grid, specifically, when the real-time power of the power grid includes the active real-time power and the reactive real-time power, the neural network processor 202 is configured to calculate the reference active power of the power grid according to the historical active power of the power grid, and calculate the reference reactive power of the power grid according to the historical reactive power of the power grid, it should be understood that the method of the neural network processor 202 calculating the reference active power, the reference reactive power and the reference output voltage may refer to the existing algorithms.
The generator control module PID is used for obtaining PWM power control signals according to the first real-time output current, the second real-time output current, the difference between the active reference power of the power grid and the active real-time power of the power grid, the difference between the reactive reference power of the power grid and the reactive real-time power of the power grid, and the difference between the reference output voltage and the real-time output voltage. The PWM power control signal is positively correlated with the first real-time output current, the second real-time output current, the difference between the active reference power of the power grid and the active real-time power of the power grid, the difference between the reactive reference power of the power grid and the reactive real-time power of the power grid, and the difference between the reference output voltage and the real-time output voltage. Specifically, the PWM power control signal may be calculated according to a PID (a controller) converter provided in the central processing unit.
In this embodiment, the voltage and power obtained by the cpu 201 according to the real-time current, voltage and power combined with the voltage and power obtained by the neural network 202 according to the historical data can be calculated to obtain the PWM power control signal for inputting to the power grid, so that stable control of the real-time power input to the power grid can be realized.
Example 4
The present embodiment provides a fan control system, and the structure of the fan apparatus 10 in the present embodiment can be referred to embodiment 3 based on embodiment 1, embodiment 2 or embodiment 3.
The target data in this embodiment includes the pitch angle of the blades.
The central processing unit 201 is configured to collect real-time power of the power grid and real-time rotation speed of the blades, and specifically, the real-time power of the power grid includes real-time active power and real-time reactive power.
The neural network processor 202 is configured to calculate a reference power of the power grid according to a historical power of the power grid, and specifically, the neural network processor 202 is configured to calculate a reference active power of the power grid according to a historical active power of the power grid, and calculate a reference reactive power of the power grid according to a historical reactive power of the power grid. The central processing unit 201 is further configured to obtain a pitch angle of the blade according to the real-time rotation speed of the blade and a difference between the reference power of the power grid and the real-time power of the power grid.
The central processing unit 201 is further configured to obtain a pitch angle of the blade according to a difference between the reference rotational speed of the fan device and the real-time rotational speed of the fan device, a difference between the reference active power of the power grid and the real-time active power of the power grid, and a difference between the reference reactive power of the power grid and the real-time reactive power of the power grid. Specifically, the pitch angle of the blade may be calculated by a PID converter provided in the central processing unit.
The pitch angle of the blade is inversely related to the difference between the reference rotating speed of the fan equipment and the real-time rotating speed of the fan equipment, and is positively related to the difference between the reference active power of the power grid and the real-time active power of the power grid and the difference between the reference reactive power of the power grid and the real-time reactive power of the power grid.
Fig. 4 shows a schematic diagram of the control principle of the fan control system in this embodiment in a specific scenario, where the central processor 201 may specifically include a fan control module PID for controlling the pitch angle of the blades (i.e. the blade angle θ shown in the figure).
Specifically, the neural network processor (not shown in the figure) calculates reference active power P grid reference value of the power grid according to historical active power of the power grid, and transmits the reference active power P grid reference value to the fan control module PID, the fan control module PID can calculate a difference value between the real active power P grid and the reference active power P grid reference value generated by the power grid acquired in real time, and can calculate a target pitch angle of the blade according to real-time reactive power Q grid output by the power grid acquired in real time and real-time rotating speed Ω of fan equipment, so that the current pitch angle of the blade can be adjusted through the pitch system, and further electric energy used by the power supply network is output through a transmission system, a generator, a converter, a filter and a transformer.
It should be understood that the above scenario is merely illustrative of the principles of the present embodiment, and should not be taken as a limitation of the present embodiment, for example, the fan control module PID may further calculate a difference between the real-time reactive power Q grid generated by the power grid collected in real time and the reference reactive power Q grid reference value calculated by the neural network processor, calculate the target pitch angle of the blade according to the difference, and calculate the difference between the target pitch angle of the blade and the reference pitch angle of the blade according to the difference between P grid reference value and P grid and the difference between the target pitch angle of the blade and the reference pitch angle of the fan device calculated by the neural network processor, and calculate the difference between the target pitch angle of the blade according to the difference between P grid reference value and P grid and the difference between Q grid reference value and Q grid. In this embodiment, the reference rotational speed and the reference power obtained by analyzing the rotational speed of the blade and the power of the power grid according to the historical data by the central processing unit 201 and the neural network processor 202 can be used to realize stable control of the pitch angle of the blade.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.

Claims (8)

1. The fan control system is characterized by comprising a control device, wherein the control device comprises a central processing unit and a neural network processor;
The central processing unit executes a real-time task and is used for collecting and processing real-time data of the fan equipment, and the central processing unit is also used for sending the real-time data processed at the current moment to the neural network processor;
The neural network processor performs a non-real-time task for analyzing historical data to generate auxiliary data and transmitting the auxiliary data to the central processor, wherein the historical data is the real-time data from the central processor after a plurality of processes received at a plurality of moments before the current moment;
The central processing unit is also used for generating target data according to the auxiliary data and the real-time data and acting the target data on the fan equipment to control the fan equipment to operate;
The fan equipment comprises a generator and a current transformer, wherein the generator is connected with the current transformer, and the current transformer is connected with a power grid; the target data comprises a PWM power control signal, wherein the PWM power control signal is used for controlling the real-time power of the power grid;
the central processing unit is used for collecting a first real-time output current of the generator, a second real-time output current of the converter and real-time power of the power grid;
The neural network processor is used for calculating the reference power of the power grid according to the historical power of the power grid;
the central processing unit is used for acquiring the PWM power control signal according to the first real-time output current, the second real-time output current and the difference between the reference power of the power grid and the real-time power of the power grid.
2. The blower control system of claim 1, further comprising a cloud platform, the neural network processor further configured to send the analyzed data to the cloud platform;
The cloud platform is used for carrying out auxiliary operation on the analyzed data to generate auxiliary data, and is also used for sending the auxiliary data to the neural network processor.
3. The fan control system of claim 1, wherein the current transformer comprises a machine side current transformer and a grid side current transformer, the machine side current transformer being connected to the grid side current transformer, the generator being connected to the machine side current transformer, the grid side current transformer being connected to the grid;
The second real-time output current is the output current of the grid-side converter;
the central processing unit is also used for collecting the real-time output voltage of the machine side converter;
The neural network processor is further used for calculating a reference output voltage of the machine side converter according to the historical output voltage of the machine side converter;
The central processing unit is used for acquiring the PWM power control signal according to the first real-time output current, the second real-time output current, the difference between the reference power of the power grid and the real-time power of the power grid, and the difference between the reference output voltage of the power grid and the real-time output voltage.
4. The fan control system of claim 3, wherein the reference power of the power grid comprises an active reference power and a reactive reference power, and the real-time power of the power grid comprises an active real-time power and a reactive real-time power;
The neural network processor is used for calculating the reference active power of the power grid according to the historical active power of the power grid, and calculating the reference reactive power of the power grid according to the historical reactive power of the power grid;
The central processing unit is used for acquiring the PWM power control signal according to the first real-time output current, the second real-time output current, the difference between the active reference power of the power grid and the active real-time power of the power grid, the difference between the reactive reference power of the power grid and the reactive real-time power of the power grid, and the difference between the reference output voltage and the real-time output voltage.
5. The fan control system of claim 1 or 2, wherein the fan apparatus comprises a blade and the target data comprises a pitch angle of the blade.
6. The fan control system of claim 5, wherein the central processor is configured to collect real-time power of the power grid and real-time rotational speed of the fan device;
The neural network processor is used for calculating the reference power of the power grid according to the historical power of the power grid and calculating the reference rotating speed of the fan equipment according to the historical rotating speed of the fan equipment;
The central processing unit is also used for acquiring the pitch angle of the blade according to the difference between the reference rotating speed of the fan equipment and the real-time rotating speed of the fan equipment and the difference between the reference power of the power grid and the real-time power of the power grid.
7. The fan control system of claim 6, wherein the reference power of the power grid comprises an active reference power and a reactive reference power, and the real-time power of the power grid comprises an active real-time power and a reactive real-time power;
The neural network processor is used for calculating the reference active power of the power grid according to the historical active power of the power grid, and calculating the reference reactive power of the power grid according to the historical reactive power of the power grid;
The central processing unit is further used for obtaining the pitch angle of the blade according to the difference between the reference rotating speed of the fan equipment and the real-time rotating speed of the fan equipment, the difference between the reference active power of the power grid and the real-time active power of the power grid and the difference between the reference reactive power of the power grid and the real-time reactive power of the power grid.
8. The fan control system of claim 1, wherein the central processor and the neural network processor communicate via an underlying bus.
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Publication number Priority date Publication date Assignee Title
CN104454347A (en) * 2014-11-28 2015-03-25 云南电网公司电力科学研究院 Method for controlling independent pitch angle of pitch-variable control wind driven generator
CN106774276A (en) * 2017-01-18 2017-05-31 河海大学 Wind power plant automatic electricity generation control system test platform
CN109586338A (en) * 2018-12-04 2019-04-05 中国电力科学研究院有限公司 The control method and device of current transformer in a kind of double-fed wind power system
CN110460250A (en) * 2019-05-23 2019-11-15 淮阴工学院 A kind of Three-Phase PWM Rectifier direct Power Control method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104454347A (en) * 2014-11-28 2015-03-25 云南电网公司电力科学研究院 Method for controlling independent pitch angle of pitch-variable control wind driven generator
CN106774276A (en) * 2017-01-18 2017-05-31 河海大学 Wind power plant automatic electricity generation control system test platform
CN109586338A (en) * 2018-12-04 2019-04-05 中国电力科学研究院有限公司 The control method and device of current transformer in a kind of double-fed wind power system
CN110460250A (en) * 2019-05-23 2019-11-15 淮阴工学院 A kind of Three-Phase PWM Rectifier direct Power Control method

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